This is a fully remote position, open to applicants in Costa Rica.

• Evaluate current ETL validation processes to pinpoint opportunities for automation.

• Create AI-driven validation frameworks tailored for data pipelines.

• Implement anomaly detection systems to ensure ongoing data quality monitoring.

• Analyze source data in comparison to ETL outputs to uncover and monitor discrepancies.

• Automate checks for record count, completeness, and consistency across various pipelines.

• Produce AI-assisted validation reports that articulate identified issues in clear, actionable language.

• Embed AI validation functionalities into AWS data platforms and workflows, including Glue, Lambda, Redshift, Athena, and S3.

• Assess the application of AI for comprehensive QA analysis of deliverables.

• Partner with data engineering, QA, and business teams to establish data quality rules, thresholds, and SLAs.

• Deliver an AI-enhanced data validation platform.

• Develop an automated framework for ETL quality control.

• Create anomaly detection models along with monitoring dashboards.

• Generate validation reports and capabilities for root-cause analysis.

• Define key performance indicators (KPIs) for data quality and provide governance recommendations.

• Establish a roadmap for extending AI-driven validation across additional workloads.

• A Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or a related field (or equivalent experience).

• Over 5 years of experience in data engineering, ETL development, or data quality roles.

• Proficient programming skills in Python and advanced SQL.

• Practical experience with Amazon Bedrock and developing generative AI solutions (prompt design, LLM-based analysis, and reporting).

• Hands-on experience with AWS Lambda, AWS Glue, Amazon Redshift, Amazon Athena, and Amazon S3.

• Applied knowledge of machine learning and anomaly detection techniques in data contexts.

• Strong grasp of data quality and data governance principles.

• Experience in crafting source-to-target reconciliation and ETL testing strategies.

• Comprehensive understanding of Agile methodologies, SDLC, and contemporary engineering best practices.

• Remote work opportunities.

• An environment that promotes work-life balance.

• Customized career development plans.

• Training programs available.

• A diverse and multicultural global team.

• Collaboration within multidisciplinary teams.

• High-impact projects with leading industry clients.

Data Analyst

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